What should be saved in SARSA prioritized sweeping?
SARSA is an on-policy method. Your historical choices of action will have been made using older Q values, and thus from a different policy. In addition, the action that was taken at the time may not have been typical for the agent (it may have been exploring). So you don't usually want to re-use historical action choices to calculate TD targets in single-step SARSA, because these may introduce bias.

Provided you are performing single-step SARSA, then you can re-generate the action choice sampling from the current best policy. This is similar to generating the max Q value in the TD target value $R_{t+1} + \text{max}_{a'} Q(S_{t+1},a')$. 

You could do this using a regular SARSA sampling the action choice:

$$R_{t+1} + Q(S_{t+1}, a' \sim \pi(\cdot|S_{t+1}) )$$

Or you could use Expected SARSA and take a weighted mean of all possible actions:

$$R_{t+1} + \sum_{a' \in \mathcal{A}(S_{t+1})} \pi(a'|S_{t+1})Q(S_{t+1}, a')$$

Technically these would both be on-policy with respect to evaluating the TD Target, but off-policy with respect to the distribution of $S_t, A_t$ that you are running the update for. That's already the case due to prioritised sweeping focussing more updates on certain transitions, but could be a big difference when using a neural network to approximate Q. Bear in mind that making TD learning methods off-policy can have a negative impact on stability.

If you want to process multi-step updates, then you do have to reference $A_{t+1}$ and adjust for when the historical data makes a different action choice than your current estimate of the best policy. This would commonly use importance sampling. This is true for Q-learning as well however, so there would still be no difference in what you store between Q-learning and SARSA.